One of the practical challenges for machine learning models is that the logic they use to classify content data is often concealed from users, causing skepticism, mistrust, and difficulty to understand why a machine learning model classifies an input in a particular way as opposed to another. In addition, building supervised machine learning models can be a time consuming task that involves the collection of training sets that are representative of the different types of inputs and outputs expected to be processed by a machine learning model. Once a machine learning model is trained and deployed, it can be difficult to identify and repair machine learning errors caused by underfitting or overfitting the machine learning model.
Therefore, a need exists for methods and apparatus to rapidly train and identify logic used by machine learning models to generate outputs.